We use our neural network to predict, given the optimal light intensity, key rates of discrete-modulated CV-QKD at different distances and different excess noises after training the neural network under \(\gamma =0.20\)  and \(\epsilon =0.80\)  according to our method described above. As shown in Fig. REF , we compare the key rates with the corresponding key rates obtained by the numerical method in Refs. [1]}, [2]}. The results show that all key rates predicted by the neural network are strictly lower than those obtained by the numerical method. It is worth noting that the relative deviations between them are basically within \(20\%\)  (Relevant data can be found in Appendix C).
